Using historical maps to analyze two hundred years of land cover changes: case study of Sorrento peninsula (south Italy)

Pindozzi, Stefania; Cervelli, Elena; Capolupo, Alessandra; Okello, Collins; Boccia, Lorenzo

doi:10.1080/15230406.2015.1072736

Cited by 41 publications

(26 citation statements)

References 32 publications

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“…Although TGI was proposed to assess vegetation zones, it was strongly affected by the scale and by chlorophyll content, showing promising results only applying high-resolution images as input. This resulted in its inability in identifying vegetation using medium-resolution data provided by Landsat missions [132]. Moreover, although Automated Water Extraction Index (shadow version) (AWEIsh) and Ashburn Vegetation Index (AVI) had really high overall accuracy, equal to 91.46 and 99.78, respectively, they could detect a few of LC/LU classes: The former detected water and built-up areas, the latter detected only water.…”

Section: Discussionmentioning

confidence: 99%

Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

2020

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Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

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Section: Discussionmentioning

confidence: 99%

Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

2020

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“…Deforestation continued in the second half of the 19 th century in much of the Mediterranean (Williams, 2006), but there were exceptions. In the Sorrento Peninsula in southern Italy (Pindozzi et al, 2016), in the Maresme district on the Mediterranean coast of Spain (Parcerisas et al, 2012), and in Catalonia (Cervera et al, 2015) forest areas have remained largely the same due to inaccessibility. In cases of afforestation in southern France (Taillefumier and Piégay, 2003), forests increased and anthropogenic afforestation intensified in the Mediterranean in the first half of the 20 th century (Hughes, 2005).…”

Section: Rezultati I Raspravamentioning

confidence: 99%

Forest cover changes in the Ravni Kotari Region in the second half of the 19th century

Blaće

2019

Hrv. geogr. glas.

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“…Each map reported different land use land cover categories and some categories did not appear in some of the maps. In order to carry out a direct comparison of land use maps, it was necessary to apply a thematic generalization [5], [25], [50], [51]. In this way, we created six classes of land use categories reported on the reference maps of 1978 and 2015 (croplands, grasslands, woodlands, water bodies, unproductive lands and artificial surfaces) and just five classes of land use categories reported on the reference map of 1856.…”

Section: Thematic Generalization and Land Use Land Cover Analysismentioning

confidence: 99%

Geospatial Analysis of Land Use Dynamics Using Historical Maps and Gis Techniques. Case Study of Rădăuți, Romania

Horodnic¹,

Graur²,

Afloari³

et al. 2018

Proceedings 2018

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Historical reconstruction of land-use and land cover dynamic often require comparing maps derived from different sources. Geographic information systems allow the extraction and quantitative analysis of information from historical maps. This study refers to the diachronic analysis of land use dynamics in the geographical area of Rădăuți municipality, territorial-administrative unit located in the northeastern part of Suceava county, Romania. The analysis is based on the use of geospatial techniques in extracting information from historical maps, for emphasizing the land cover and land use dynamic from a spatial and temporal point of view. The cartographic and statistic analysis is based on the identification of a variety of land use categories: croplands, woodlands, water bodies, artificial surfaces, degraded and unproductive lands. Selection of historical maps at a large scale (Austrian cadastral maps, scale 1:2 880; the topographical plans, scale 1:5 000 and ortophotos, scale 1:5 000, updated with aerial images using Terra Incognita 2.41 software) allows a detalied analysis on the land use dynamics in the above-mentioned area. Complementary, temporal dyanamic of the land use categories is highlighted by the choice of some benchmark years, i.e. 1856, 1978 and 2015 corresponding to a different historical, geographic, social and economic context. The results obtained emphasize the territorial distribution and the dynamic of land use categories conditioned by natural and socioeconomic driving factors which influenced with different frequency and intensity during of 159 years. The analysis revealed that the urban landscape has been very dynamic, displaying significant changes in most type of land use, most notably in the case of urban built-up, which emphasize substantial increases, from 134,93 hectares in 1856 to 773,42 hectares in 2015. The analysis of land use dynamic is very useful for planners, because it can argue the best decisions regarding sustainable development of urban areas.

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Using historical maps to analyze two hundred years of land cover changes: case study of Sorrento peninsula (south Italy)

Cited by 41 publications

References 32 publications

Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Forest cover changes in the Ravni Kotari Region in the second half of the 19th century

Geospatial Analysis of Land Use Dynamics Using Historical Maps and Gis Techniques. Case Study of Rădăuți, Romania

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